Since it is evidently meaningful to form a contingency table from A and C, and from B and C, I suspect what you call "datasets" might be more commonly referred to as variables within a single dataset. Moreover, these seem to be categorical variables. I gather you want to test if there is an association between C and either A or B. You can determine that within a single model—that is, Fisher's exact test is inappropriate here and you don't need to combine p values. Instead, the appropriate analysis is a log-linear model.
Here is a quick R demonstration:
d = data.frame(A= rep(c("yes","no"), each=40),
B=rep(rep(c("yes","no"), each=20), 2),
C= c(rep(c("yes","no"), times=c( 5,15)),
rep(c("yes","no"), times=c(10,10)),
rep(c("yes","no"), times=c(10,10)),
rep(c("yes","no"), times=c(15, 5)) ) )
tab = table(d)
margin.table(tab, margin=c(1,3))
# C
# A no yes
# no 15 25
# yes 25 15
margin.table(tab, margin=c(2,3))
# C
# B no yes
# no 15 25
# yes 25 15
margin.table(tab, margin=c(1,2))
# B
# A no yes
# no 20 20
# yes 20 20
library(MASS) # we use this package to get the loglm() function
loglm(~C + A*B, tab)
# Call:
# loglm(formula = ~C + A * B, data = tab)
#
# Statistics:
# X^2 df P(> X^2)
# Likelihood Ratio 10.46496 3 0.01500047
# Pearson 10.00000 3 0.01856614
I have a more extensive discussion here: $\chi^2$ of multidimensional data.